High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

CHOSEN 280x420		96.80 35	96.85 27	96.66 91	97.85 113	94.42 49	94.76 299	98.36 23	92.50 61	95.62 95	97.52 140	97.92 1	97.38 214	98.31 33	98.80 94	98.20 171

GG-mvs-BLEND				96.98 68	96.53 156	94.81 38	87.20 336	97.74 68		93.91 122	96.40 180	96.56 2	96.94 227	95.08 93	98.95 88	99.20 107

gg-mvs-nofinetune		90.00 189	87.71 210	96.89 79	96.15 172	94.69 43	85.15 342	97.74 68	68.32 344	92.97 135	60.16 353	96.10 3	96.84 229	93.89 113	98.87 89	99.14 110

MSP-MVS		97.77 8	98.18 2	96.53 97	99.54 36	90.14 138	99.41 54	97.70 79	95.46 17	98.60 21	99.19 32	95.71 4	99.49 104	98.15 36	99.85 11	99.95 11
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
baseline2		94.04 105	93.80 106	94.74 154	93.07 262	90.25 135	98.12 195	98.16 33	89.86 121	86.53 205	96.95 164	95.56 5	98.05 174	91.44 141	94.53 154	95.93 214

OPU-MVS				99.49 2	99.64 20	98.51 2	99.77 9				99.19 32	95.12 6	99.97 20	99.90 1	99.92 3	99.99 1

tttt0517		93.30 128	93.01 120	94.17 173	95.57 187	86.47 218	98.51 156	97.60 98	85.99 222	90.55 164	97.19 153	94.80 7	98.31 158	85.06 210	91.86 184	97.74 179

thisisatest0530		94.00 106	93.52 109	95.43 135	95.76 182	90.02 147	98.99 101	97.60 98	86.58 214	91.74 144	97.36 146	94.78 8	98.34 157	86.37 197	92.48 174	97.94 177

thisisatest0515		94.75 88	94.19 90	96.43 101	96.13 176	92.64 88	99.47 40	97.60 98	87.55 197	93.17 130	97.59 138	94.71 9	98.42 156	88.28 177	93.20 163	98.24 168

test_0728_THIRD									93.01 49	99.07 8	99.46 11	94.66 10	99.97 20	99.25 11	99.82 15	99.95 11

ET-MVSNet_ETH3D		92.56 144	91.45 152	95.88 120	96.39 160	94.13 55	99.46 45	96.97 179	92.18 72	66.94 341	98.29 115	94.65 11	94.28 319	94.34 109	83.82 237	99.24 103

MVSTER		92.71 138	92.32 131	93.86 184	97.29 130	92.95 82	99.01 99	96.59 192	90.09 117	85.51 210	94.00 216	94.61 12	96.56 241	90.77 151	83.03 243	92.08 244

DWT-MVSNet_test		94.36 100	93.95 101	95.62 128	96.99 143	89.47 159	96.62 263	97.38 141	90.96 95	93.07 133	97.27 147	93.73 13	98.09 169	85.86 205	93.65 161	99.29 98

DPM-MVS		97.86 7	97.25 17	99.68 1	98.25 102	99.10 1	99.76 12	97.78 63	96.61 4	98.15 31	99.53 7	93.62 14	100.00 1	91.79 139	99.80 23	99.94 14

SED-MVS		98.18 2	98.10 4	98.41 14	99.63 21	95.24 21	99.77 9	97.72 74	94.17 24	99.30 4	99.54 3	93.32 15	99.98 10	99.70 2	99.81 19	99.99 1

test_241102_ONE					99.63 21	95.24 21		97.72 74	94.16 26	99.30 4	99.49 10	93.32 15	99.98 10

DPE-MVS		98.11 5	98.00 5	98.44 13	99.50 43	95.39 18	99.29 66	97.72 74	94.50 20	98.64 20	99.54 3	93.32 15	99.97 20	99.58 7	99.90 5	99.95 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		98.07 6	98.00 5	98.29 15	99.66 15	95.20 26	99.72 14	97.47 128	93.95 29	99.07 8	99.46 11	93.18 18	99.97 20	99.64 5	99.82 15	99.69 60
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					99.66 15	95.20 26	99.77 9	97.70 79	93.95 29	99.35 3	99.54 3	93.18 18

test_241102_TWO								97.72 74	94.17 24	99.23 6	99.54 3	93.14 20	99.98 10	99.70 2	99.82 15	99.99 1

CNVR-MVS		98.46 1	98.38 1	98.72 6	99.80 4	96.19 12	99.80 8	97.99 43	97.05 3	99.41 2	99.59 2	92.89 21	100.00 1	98.99 13	99.90 5	99.96 8

MCST-MVS		98.18 2	97.95 7	98.86 3	99.85 3	96.60 7	99.70 17	97.98 44	97.18 2	95.96 84	99.33 21	92.62 22	100.00 1	98.99 13	99.93 1	99.98 6

NCCC		98.12 4	98.11 3	98.13 20	99.76 6	94.46 46	99.81 6	97.88 49	96.54 5	98.84 14	99.46 11	92.55 23	99.98 10	98.25 34	99.93 1	99.94 14

SteuartSystems-ACMMP		97.25 17	97.34 15	97.01 62	97.38 127	91.46 104	99.75 13	97.66 83	94.14 28	98.13 32	99.26 24	92.16 24	99.66 80	97.91 40	99.64 43	99.90 20
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		97.44 15	97.46 11	97.39 49	99.12 72	93.49 67	98.52 153	97.50 123	94.46 21	98.99 10	98.64 96	91.58 25	99.08 139	98.49 24	99.83 13	99.60 73
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TSAR-MVS + GP.		96.95 29	96.91 25	97.07 59	98.88 86	91.62 100	99.58 29	96.54 199	95.09 18	96.84 65	98.63 98	91.16 26	99.77 67	99.04 12	96.42 131	99.81 31

EPP-MVSNet		93.75 114	93.67 107	94.01 180	95.86 179	85.70 242	98.67 135	97.66 83	84.46 246	91.36 153	97.18 154	91.16 26	97.79 188	92.93 130	93.75 160	98.53 152

HPM-MVS++		97.72 9	97.59 9	98.14 19	99.53 41	94.76 39	99.19 70	97.75 66	95.66 13	98.21 30	99.29 22	91.10 28	99.99 5	97.68 43	99.87 7	99.68 61

旧先验1					98.97 80	92.90 83		97.74 68			99.15 41	91.05 29			99.33 70	99.60 73

train_agg		97.20 22	97.08 19	97.57 42	99.57 33	93.17 72	99.38 56	97.66 83	90.18 113	98.39 27	99.18 35	90.94 30	99.66 80	98.58 22	99.85 11	99.88 24

test_8					99.55 35	93.07 76	99.37 59	97.64 89	90.18 113	98.36 29	99.19 32	90.94 30	99.64 86

TEST9					99.57 33	93.17 72	99.38 56	97.66 83	89.57 133	98.39 27	99.18 35	90.88 32	99.66 80

SD-MVS		97.51 12	97.40 14	97.81 32	99.01 79	93.79 61	99.33 64	97.38 141	93.73 40	98.83 15	99.02 57	90.87 33	99.88 44	98.69 17	99.74 28	99.77 44
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
agg_prior1		97.12 24	97.03 21	97.38 50	99.54 36	92.66 84	99.35 61	97.64 89	90.38 107	97.98 40	99.17 37	90.84 34	99.61 89	98.57 23	99.78 27	99.87 27

APDe-MVS		97.53 11	97.47 10	97.70 36	99.58 29	93.63 62	99.56 32	97.52 117	93.59 43	98.01 39	99.12 46	90.80 35	99.55 94	99.26 10	99.79 25	99.93 17

RRT_test8_iter05		91.04 170	90.40 172	92.95 200	96.20 170	89.75 154	98.97 103	96.38 207	88.52 161	82.00 252	93.51 231	90.69 36	96.73 235	90.43 153	76.91 276	92.38 232

IB-MVS	89.43 6	92.12 151	90.83 164	95.98 118	95.40 194	90.78 124	99.81 6	98.06 38	91.23 91	85.63 209	93.66 226	90.63 37	98.78 145	91.22 143	71.85 317	98.36 163
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
ETH3 D test6400		97.67 10	97.33 16	98.69 7	99.69 9	96.43 9	99.63 25	97.73 72	91.05 92	98.66 19	99.53 7	90.59 38	99.71 73	99.32 8	99.80 23	99.91 18

segment_acmp												90.56 39

test_prior3		97.07 26	97.09 18	97.01 62	99.58 29	91.77 95	99.57 30	97.57 107	91.43 85	98.12 34	98.97 63	90.43 40	99.49 104	98.33 30	99.81 19	99.79 34

test_prior2							99.57 30		91.43 85	98.12 34	98.97 63	90.43 40		98.33 30	99.81 19

xxxxxxxxxxxxxcwj		97.51 12	97.42 13	97.78 34	99.34 53	93.85 59	99.65 23	95.45 272	95.69 11	98.70 17	99.42 17	90.42 42	99.72 71	98.47 25	99.65 40	99.77 44

SF-MVS		97.22 21	96.92 24	98.12 22	99.11 73	94.88 32	99.44 48	97.45 130	89.60 131	98.70 17	99.42 17	90.42 42	99.72 71	98.47 25	99.65 40	99.77 44

ETH3D-3000-0.1		97.29 16	97.01 22	98.12 22	99.18 69	94.97 30	99.47 40	97.52 117	89.85 122	98.79 16	99.46 11	90.41 44	99.69 75	98.78 15	99.67 38	99.70 57

DeepPCF-MVS	93.56 1	96.55 41	97.84 8	92.68 208	98.71 92	78.11 320	99.70 17	97.71 78	98.18 1	97.36 52	99.76 1	90.37 45	99.94 33	99.27 9	99.54 57	99.99 1

SMA-MVS		97.24 18	96.99 23	98.00 27	99.30 60	94.20 53	99.16 75	97.65 88	89.55 135	99.22 7	99.52 9	90.34 46	99.99 5	98.32 32	99.83 13	99.82 30
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
ZD-MVS					99.67 13	93.28 70		97.61 96	87.78 188	97.41 50	99.16 39	90.15 47	99.56 93	98.35 29	99.70 35

CostFormer		92.89 136	92.48 130	94.12 175	94.99 213	85.89 237	92.89 315	97.00 178	86.98 205	95.00 105	90.78 277	90.05 48	97.51 208	92.92 131	91.73 188	98.96 121

testtj		97.23 20	97.05 20	97.75 35	99.75 7	93.34 69	99.16 75	97.74 68	91.28 89	98.40 26	99.29 22	89.95 49	99.98 10	98.20 35	99.70 35	99.94 14

MSLP-MVS++		97.50 14	97.45 12	97.63 38	99.65 19	93.21 71	99.70 17	98.13 36	94.61 19	97.78 45	99.46 11	89.85 50	99.81 62	97.97 38	99.91 4	99.88 24

9.14			96.87 26		99.34 53		99.50 38	97.49 125	89.41 138	98.59 22	99.43 16	89.78 51	99.69 75	98.69 17	99.62 47

Regformer-1		96.97 28	96.80 30	97.47 44	99.46 47	93.11 74	98.89 111	97.94 45	92.89 54	96.90 58	99.02 57	89.78 51	99.53 97	97.06 49	99.26 76	99.75 48

Regformer-2		96.94 31	96.78 31	97.42 46	99.46 47	92.97 81	98.89 111	97.93 46	92.86 56	96.88 59	99.02 57	89.74 53	99.53 97	97.03 50	99.26 76	99.75 48

PAPM		96.35 47	95.94 55	97.58 40	94.10 233	95.25 20	98.93 106	98.17 31	94.26 23	93.94 121	98.72 89	89.68 54	97.88 182	96.36 67	99.29 74	99.62 71

ETH3D cwj APD-0.16		96.94 31	96.58 36	98.01 26	98.62 95	94.73 41	99.13 87	97.38 141	88.44 168	98.53 24	99.39 19	89.66 55	99.69 75	98.43 27	99.61 51	99.61 72

CSCG		94.87 85	94.71 80	95.36 137	99.54 36	86.49 217	99.34 63	98.15 34	82.71 274	90.15 172	99.25 25	89.48 56	99.86 52	94.97 97	98.82 93	99.72 54

PHI-MVS		96.65 38	96.46 38	97.21 56	99.34 53	91.77 95	99.70 17	98.05 39	86.48 217	98.05 36	99.20 31	89.33 57	99.96 27	98.38 28	99.62 47	99.90 20

TESTMET0.1,1		93.82 112	93.26 114	95.49 132	95.21 198	90.25 135	99.15 81	97.54 113	89.18 142	91.79 143	94.87 204	89.13 58	97.63 201	86.21 198	96.29 136	98.60 150

APD-MVS		96.95 29	96.72 32	97.63 38	99.51 42	93.58 63	99.16 75	97.44 134	90.08 118	98.59 22	99.07 51	89.06 59	99.42 115	97.92 39	99.66 39	99.88 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CDS-MVSNet		93.47 121	93.04 119	94.76 152	94.75 222	89.45 160	98.82 117	97.03 175	87.91 185	90.97 158	96.48 178	89.06 59	96.36 255	89.50 161	92.81 169	98.49 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Patchmatch-test		86.25 250	84.06 265	92.82 202	94.42 227	82.88 281	82.88 351	94.23 309	71.58 333	79.39 284	90.62 286	89.00 61	96.42 252	63.03 337	91.37 194	99.16 109

CDPH-MVS		96.56 40	96.18 45	97.70 36	99.59 28	93.92 57	99.13 87	97.44 134	89.02 147	97.90 43	99.22 29	88.90 62	99.49 104	94.63 104	99.79 25	99.68 61

MG-MVS		97.24 18	96.83 29	98.47 12	99.79 5	95.71 15	99.07 91	99.06 9	94.45 22	96.42 77	98.70 92	88.81 63	99.74 70	95.35 88	99.86 10	99.97 7

patchmatchnet-post											84.86 335	88.73 64	96.81 231

Regformer-3		96.50 42	96.36 41	96.91 73	99.34 53	91.72 98	98.71 126	97.90 48	92.48 62	96.00 81	98.95 70	88.60 65	99.52 100	96.44 65	98.83 91	99.49 83

test12				97.83 31	99.33 59	94.45 47		97.55 110		97.56 46		88.60 65	99.50 103		99.71 34	99.55 77

1121		95.19 80	94.45 85	97.42 46	98.88 86	92.58 89	96.22 276	97.75 66	85.50 229	96.86 62	99.01 61	88.59 67	99.90 40	87.64 185	99.60 52	99.79 34

MVS_111021_HR		96.69 36	96.69 33	96.72 87	98.58 97	91.00 120	99.14 84	99.45 1	93.86 35	95.15 102	98.73 87	88.48 68	99.76 68	97.23 48	99.56 55	99.40 90

Regformer-4		96.45 45	96.33 43	96.81 80	99.34 53	91.44 105	98.71 126	97.88 49	92.43 63	95.97 83	98.95 70	88.42 69	99.51 101	96.40 66	98.83 91	99.49 83

sam_mvs1												88.39 70				98.84 132

PatchmatchNet		92.05 153	91.04 157	95.06 145	96.17 171	89.04 164	91.26 328	97.26 147	89.56 134	90.64 163	90.56 290	88.35 71	97.11 220	79.53 260	96.07 141	99.03 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		92.78 137	92.16 136	94.65 157	96.27 164	87.45 197	91.83 323	97.10 169	89.10 145	94.68 109	90.69 281	88.22 72	97.73 197	89.78 159	91.80 186	98.77 142

DELS-MVS		97.12 24	96.60 35	98.68 8	98.03 110	96.57 8	99.84 3	97.84 53	96.36 8	95.20 101	98.24 116	88.17 73	99.83 57	96.11 72	99.60 52	99.64 67
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
testdata				95.26 141	98.20 104	87.28 202		97.60 98	85.21 232	98.48 25	99.15 41	88.15 74	98.72 151	90.29 154	99.45 62	99.78 38

原ACMM1				96.18 109	99.03 78	90.08 141		97.63 93	88.98 148	97.00 57	98.97 63	88.14 75	99.71 73	88.23 178	99.62 47	98.76 143

新几何1				97.40 48	98.92 84	92.51 91		97.77 65	85.52 227	96.69 72	99.06 53	88.08 76	99.89 43	84.88 213	99.62 47	99.79 34

test-mter		93.27 130	92.89 122	94.40 165	94.94 216	87.27 203	99.15 81	97.25 148	88.95 150	91.57 147	94.04 212	88.03 77	97.58 204	85.94 202	96.13 137	98.36 163

JIA-IIPM		85.97 253	84.85 253	89.33 281	93.23 259	73.68 333	85.05 343	97.13 164	69.62 340	91.56 149	68.03 351	88.03 77	96.96 225	77.89 273	93.12 164	97.34 189

test_yl		95.27 78	94.60 82	97.28 53	98.53 98	92.98 79	99.05 94	98.70 16	86.76 211	94.65 110	97.74 130	87.78 79	99.44 112	95.57 84	92.61 171	99.44 87

DCV-MVSNet		95.27 78	94.60 82	97.28 53	98.53 98	92.98 79	99.05 94	98.70 16	86.76 211	94.65 110	97.74 130	87.78 79	99.44 112	95.57 84	92.61 171	99.44 87

PAPM_NR		95.43 73	95.05 77	96.57 95	99.42 49	90.14 138	98.58 150	97.51 120	90.65 100	92.44 139	98.90 76	87.77 81	99.90 40	90.88 148	99.32 71	99.68 61

RRT_MVS		91.95 154	91.09 155	94.53 161	96.71 153	95.12 28	98.64 139	96.23 218	89.04 146	85.24 212	95.06 201	87.71 82	96.43 251	89.10 172	82.06 250	92.05 246

HFP-MVS		96.42 46	96.26 44	96.90 74	99.69 9	90.96 121	99.47 40	97.81 58	90.54 103	96.88 59	99.05 54	87.57 83	99.96 27	95.65 79	99.72 30	99.78 38

#test#		96.48 43	96.34 42	96.90 74	99.69 9	90.96 121	99.53 36	97.81 58	90.94 96	96.88 59	99.05 54	87.57 83	99.96 27	95.87 76	99.72 30	99.78 38

tpm2		91.77 156	91.09 155	93.82 186	94.83 220	85.56 245	92.51 320	97.16 161	84.00 252	93.83 124	90.66 283	87.54 85	97.17 218	87.73 184	91.55 191	98.72 144

EPNet		96.82 34	96.68 34	97.25 55	98.65 93	93.10 75	99.48 39	98.76 12	96.54 5	97.84 44	98.22 117	87.49 86	99.66 80	95.35 88	97.78 113	99.00 117
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		96.22 52	96.15 50	96.42 102	99.67 13	89.62 157	99.70 17	97.61 96	90.07 119	96.00 81	99.16 39	87.43 87	99.92 36	96.03 74	99.72 30	99.70 57

miper_enhance_ethall		90.33 180	89.70 176	92.22 213	97.12 137	88.93 168	98.35 177	95.96 232	88.60 159	83.14 232	92.33 250	87.38 88	96.18 269	86.49 196	77.89 268	91.55 262

test_post											46.00 360	87.37 89	97.11 220

XVS		96.47 44	96.37 40	96.77 81	99.62 25	90.66 129	99.43 51	97.58 104	92.41 67	96.86 62	98.96 68	87.37 89	99.87 47	95.65 79	99.43 64	99.78 38

X-MVStestdata		90.69 177	88.66 196	96.77 81	99.62 25	90.66 129	99.43 51	97.58 104	92.41 67	96.86 62	29.59 364	87.37 89	99.87 47	95.65 79	99.43 64	99.78 38

DP-MVS Recon		95.85 64	95.15 75	97.95 28	99.87 2	94.38 50	99.60 27	97.48 126	86.58 214	94.42 112	99.13 45	87.36 92	99.98 10	93.64 119	98.33 106	99.48 85

DeepC-MVS_fast	93.52 2	97.16 23	96.84 28	98.13 20	99.61 27	94.45 47	98.85 114	97.64 89	96.51 7	95.88 87	99.39 19	87.35 93	99.99 5	96.61 60	99.69 37	99.96 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PAPR		96.35 47	95.82 60	97.94 29	99.63 21	94.19 54	99.42 53	97.55 110	92.43 63	93.82 125	99.12 46	87.30 94	99.91 38	94.02 111	99.06 81	99.74 51

Patchmatch-RL test		81.90 293	80.13 296	87.23 303	80.71 349	70.12 345	84.07 348	88.19 354	83.16 266	70.57 329	82.18 340	87.18 95	92.59 333	82.28 244	62.78 337	98.98 119

sam_mvs												87.08 96

EI-MVSNet-Vis-set		95.76 70	95.63 70	96.17 111	99.14 71	90.33 133	98.49 160	97.82 55	91.92 75	94.75 107	98.88 78	87.06 97	99.48 109	95.40 87	97.17 124	98.70 146

1112_ss		92.71 138	91.55 150	96.20 108	95.56 188	91.12 113	98.48 161	94.69 298	88.29 174	86.89 201	98.50 105	87.02 98	98.66 153	84.75 214	89.77 205	98.81 137

Test_1112_low_res		92.27 149	90.97 158	96.18 109	95.53 190	91.10 115	98.47 163	94.66 299	88.28 175	86.83 203	93.50 232	87.00 99	98.65 154	84.69 215	89.74 206	98.80 138

MDTV_nov1_ep13			90.47 171		96.14 173	88.55 177	91.34 327	97.51 120	89.58 132	92.24 141	90.50 294	86.99 100	97.61 203	77.64 274	92.34 176

region2R		96.30 50	96.17 47	96.70 88	99.70 8	90.31 134	99.46 45	97.66 83	90.55 102	97.07 56	99.07 51	86.85 101	99.97 20	95.43 86	99.74 28	99.81 31

baseline1		92.61 142	91.28 153	96.58 94	97.05 141	94.63 44	97.72 220	96.20 220	89.82 123	88.56 186	96.85 169	86.85 101	97.82 186	88.42 175	80.10 258	97.30 190

SR-MVS		96.13 54	96.16 49	96.07 114	99.42 49	89.04 164	98.59 148	97.33 146	90.44 105	96.84 65	99.12 46	86.75 103	99.41 117	97.47 44	99.44 63	99.76 47

test222					98.32 101	91.21 108	98.08 200	97.58 104	83.74 255	95.87 88	99.02 57	86.74 104			99.64 43	99.81 31

test1172		95.92 62	96.07 52	95.46 133	99.42 49	87.24 207	98.51 156	97.24 150	90.29 110	96.56 76	99.12 46	86.73 105	99.36 121	97.33 47	99.42 67	99.78 38

SR-MVS-dyc-post		95.75 71	95.86 58	95.41 136	99.22 66	87.26 205	98.40 171	97.21 154	89.63 129	96.67 73	98.97 63	86.73 105	99.36 121	96.62 58	99.31 72	99.60 73

MDTV_nov1_ep13_2view						91.17 112	91.38 326		87.45 199	93.08 132		86.67 107		87.02 189		98.95 125

ETV-MVS		96.00 57	96.00 53	96.00 116	96.56 155	91.05 118	99.63 25	96.61 189	93.26 48	97.39 51	98.30 114	86.62 108	98.13 166	98.07 37	97.57 115	98.82 136

ZNCC-MVS		96.09 55	95.81 62	96.95 72	99.42 49	91.19 109	99.55 33	97.53 114	89.72 126	95.86 89	98.94 75	86.59 109	99.97 20	95.13 92	99.56 55	99.68 61

ACMMP_NAP		96.59 39	96.18 45	97.81 32	98.82 89	93.55 64	98.88 113	97.59 102	90.66 98	97.98 40	99.14 43	86.59 109	100.00 1	96.47 64	99.46 60	99.89 23

WTY-MVS		95.97 59	95.11 76	98.54 10	97.62 119	96.65 6	99.44 48	98.74 13	92.25 70	95.21 100	98.46 111	86.56 111	99.46 111	95.00 96	92.69 170	99.50 82

HY-MVS	88.56 7	95.29 77	94.23 89	98.48 11	97.72 115	96.41 10	94.03 306	98.74 13	92.42 66	95.65 94	94.76 206	86.52 112	99.49 104	95.29 90	92.97 166	99.53 78

ACMMPR		96.28 51	96.14 51	96.73 85	99.68 12	90.47 132	99.47 40	97.80 60	90.54 103	96.83 67	99.03 56	86.51 113	99.95 30	95.65 79	99.72 30	99.75 48

EPMVS		92.59 143	91.59 149	95.59 131	97.22 132	90.03 146	91.78 324	98.04 40	90.42 106	91.66 146	90.65 284	86.49 114	97.46 210	81.78 249	96.31 134	99.28 100

zzz-MVS		96.21 53	95.96 54	96.96 70	99.29 61	91.19 109	98.69 131	97.45 130	92.58 58	94.39 113	99.24 27	86.43 115	99.99 5	96.22 68	99.40 68	99.71 55

MTAPA		96.09 55	95.80 63	96.96 70	99.29 61	91.19 109	97.23 239	97.45 130	92.58 58	94.39 113	99.24 27	86.43 115	99.99 5	96.22 68	99.40 68	99.71 55

GST-MVS		95.97 59	95.66 66	96.90 74	99.49 45	91.22 107	99.45 47	97.48 126	89.69 127	95.89 86	98.72 89	86.37 117	99.95 30	94.62 105	99.22 79	99.52 79

alignmvs		95.77 69	95.00 78	98.06 25	97.35 128	95.68 16	99.71 16	97.50 123	91.50 83	96.16 80	98.61 99	86.28 118	99.00 141	96.19 70	91.74 187	99.51 81

EI-MVSNet-UG-set		95.43 73	95.29 72	95.86 121	99.07 77	89.87 149	98.43 165	97.80 60	91.78 78	94.11 118	98.77 83	86.25 119	99.48 109	94.95 98	96.45 130	98.22 169

mPP-MVS		95.90 63	95.75 64	96.38 104	99.58 29	89.41 161	99.26 67	97.41 138	90.66 98	94.82 106	98.95 70	86.15 120	99.98 10	95.24 91	99.64 43	99.74 51

EIA-MVS		95.11 81	95.27 73	94.64 158	96.34 162	86.51 216	99.59 28	96.62 188	92.51 60	94.08 119	98.64 96	86.05 121	98.24 163	95.07 94	98.50 102	99.18 108

PLC	91.07 3	94.23 103	94.01 96	94.87 149	99.17 70	87.49 195	99.25 68	96.55 197	88.43 169	91.26 154	98.21 119	85.92 122	99.86 52	89.77 160	97.57 115	97.24 192
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PGM-MVS		95.85 64	95.65 68	96.45 100	99.50 43	89.77 153	98.22 186	98.90 11	89.19 141	96.74 70	98.95 70	85.91 123	99.92 36	93.94 112	99.46 60	99.66 65

MP-MVS-pluss		95.80 67	95.30 71	97.29 52	98.95 83	92.66 84	98.59 148	97.14 162	88.95 150	93.12 131	99.25 25	85.62 124	99.94 33	96.56 62	99.48 59	99.28 100
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MVSFormer		94.71 92	94.08 95	96.61 92	95.05 211	94.87 33	97.77 216	96.17 223	86.84 208	98.04 37	98.52 103	85.52 125	95.99 276	89.83 157	98.97 85	98.96 121

lupinMVS		96.32 49	95.94 55	97.44 45	95.05 211	94.87 33	99.86 2	96.50 201	93.82 38	98.04 37	98.77 83	85.52 125	98.09 169	96.98 54	98.97 85	99.37 91

MP-MVS		96.00 57	95.82 60	96.54 96	99.47 46	90.13 140	99.36 60	97.41 138	90.64 101	95.49 96	98.95 70	85.51 127	99.98 10	96.00 75	99.59 54	99.52 79
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS_3200maxsize		95.64 72	95.65 68	95.62 128	99.24 65	87.80 188	98.42 166	97.22 153	88.93 152	96.64 75	98.98 62	85.49 128	99.36 121	96.68 57	99.27 75	99.70 57

HyFIR lowres test		93.68 117	93.29 113	94.87 149	97.57 123	88.04 185	98.18 190	98.47 21	87.57 196	91.24 155	95.05 202	85.49 128	97.46 210	93.22 126	92.82 167	99.10 112

EPNet_dtu		92.28 148	92.15 137	92.70 207	97.29 130	84.84 256	98.64 139	97.82 55	92.91 53	93.02 134	97.02 161	85.48 130	95.70 291	72.25 311	94.89 152	97.55 186
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet (Re-imp)		93.26 131	93.00 121	94.06 177	96.14 173	86.71 215	98.68 133	96.70 186	88.30 173	89.71 179	97.64 136	85.43 131	96.39 253	88.06 181	96.32 133	99.08 114

test_post1							90.74 333				41.37 363	85.38 132	96.36 255	83.16 234

RE-MVS-def			95.70 65		99.22 66	87.26 205	98.40 171	97.21 154	89.63 129	96.67 73	98.97 63	85.24 133		96.62 58	99.31 72	99.60 73

CS-MVS		95.85 64	95.86 58	95.82 122	96.80 148	89.78 152	99.84 3	96.60 190	92.60 57	96.81 69	98.70 92	85.04 134	98.25 162	97.90 41	98.43 104	99.42 89

tpm		89.67 193	88.95 189	91.82 223	92.54 267	81.43 293	92.95 314	95.92 238	87.81 187	90.50 166	89.44 309	84.99 135	95.65 292	83.67 231	82.71 246	98.38 160

HPM-MVS		95.41 75	95.22 74	95.99 117	99.29 61	89.14 162	99.17 74	97.09 170	87.28 201	95.40 97	98.48 108	84.93 136	99.38 119	95.64 83	99.65 40	99.47 86
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test-LLR		93.11 134	92.68 125	94.40 165	94.94 216	87.27 203	99.15 81	97.25 148	90.21 111	91.57 147	94.04 212	84.89 137	97.58 204	85.94 202	96.13 137	98.36 163

test0.0.03 1		88.96 202	88.61 197	90.03 264	91.09 287	84.43 261	98.97 103	97.02 176	90.21 111	80.29 272	96.31 184	84.89 137	91.93 342	72.98 308	85.70 224	93.73 221

PatchT		85.44 263	83.19 270	92.22 213	93.13 261	83.00 276	83.80 350	96.37 208	70.62 335	90.55 164	79.63 346	84.81 139	94.87 309	58.18 347	91.59 190	98.79 139

TAMVS		92.62 141	92.09 139	94.20 172	94.10 233	87.68 190	98.41 168	96.97 179	87.53 198	89.74 177	96.04 189	84.77 140	96.49 247	88.97 173	92.31 177	98.42 156

CR-MVSNet		88.83 208	87.38 215	93.16 196	93.47 252	86.24 225	84.97 344	94.20 310	88.92 153	90.76 161	86.88 328	84.43 141	94.82 311	70.64 315	92.17 181	98.41 157

Patchmtry		83.61 285	81.64 285	89.50 277	93.36 256	82.84 282	84.10 347	94.20 310	69.47 341	79.57 282	86.88 328	84.43 141	94.78 312	68.48 322	74.30 293	90.88 284

dp		90.16 186	88.83 192	94.14 174	96.38 161	86.42 219	91.57 325	97.06 172	84.76 243	88.81 184	90.19 302	84.29 143	97.43 213	75.05 292	91.35 195	98.56 151

miper_ehance_all_eth		88.94 203	88.12 206	91.40 230	95.32 195	86.93 211	97.85 212	95.55 266	84.19 249	81.97 253	91.50 264	84.16 144	95.91 283	84.69 215	77.89 268	91.36 270

MVS_111021_LR		95.78 68	95.94 55	95.28 140	98.19 106	87.69 189	98.80 119	99.26 7	93.39 45	95.04 104	98.69 94	84.09 145	99.76 68	96.96 55	99.06 81	98.38 160

tpmvs		89.16 198	87.76 208	93.35 192	97.19 133	84.75 258	90.58 334	97.36 144	81.99 285	84.56 216	89.31 312	83.98 146	98.17 164	74.85 295	90.00 204	97.12 194

API-MVS		94.78 87	94.18 92	96.59 93	99.21 68	90.06 145	98.80 119	97.78 63	83.59 259	93.85 123	99.21 30	83.79 147	99.97 20	92.37 135	99.00 84	99.74 51

cl-mvsnet2		89.57 195	88.79 193	91.91 220	97.94 112	87.62 192	97.98 205	96.51 200	85.03 237	82.37 243	91.79 258	83.65 148	96.50 245	85.96 201	77.89 268	91.61 259

Test By Simon												83.62 149

PVSNet_BlendedMVS		93.36 126	93.20 115	93.84 185	98.77 90	91.61 101	99.47 40	98.04 40	91.44 84	94.21 116	92.63 248	83.50 150	99.87 47	97.41 45	83.37 241	90.05 307

PVSNet_Blended		95.94 61	95.66 66	96.75 83	98.77 90	91.61 101	99.88 1	98.04 40	93.64 42	94.21 116	97.76 128	83.50 150	99.87 47	97.41 45	97.75 114	98.79 139

HPM-MVS_fast		94.89 84	94.62 81	95.70 127	99.11 73	88.44 180	99.14 84	97.11 166	85.82 224	95.69 93	98.47 109	83.46 152	99.32 127	93.16 127	99.63 46	99.35 92

thres200		93.69 115	92.59 128	96.97 69	97.76 114	94.74 40	99.35 61	99.36 2	89.23 140	91.21 156	96.97 163	83.42 153	98.77 146	85.08 209	90.96 196	97.39 188

tfpn200view9		93.43 123	92.27 133	96.90 74	97.68 117	94.84 35	99.18 72	99.36 2	88.45 165	90.79 159	96.90 166	83.31 154	98.75 148	84.11 224	90.69 198	97.12 194

thres400		93.39 125	92.27 133	96.73 85	97.68 117	94.84 35	99.18 72	99.36 2	88.45 165	90.79 159	96.90 166	83.31 154	98.75 148	84.11 224	90.69 198	96.61 203

thres100view900		93.34 127	92.15 137	96.90 74	97.62 119	94.84 35	99.06 93	99.36 2	87.96 183	90.47 167	96.78 170	83.29 156	98.75 148	84.11 224	90.69 198	97.12 194

thres600view7		93.18 132	92.00 140	96.75 83	97.62 119	94.92 31	99.07 91	99.36 2	87.96 183	90.47 167	96.78 170	83.29 156	98.71 152	82.93 238	90.47 202	96.61 203

PVSNet_Blended_VisFu		94.67 93	94.11 93	96.34 106	97.14 135	91.10 115	99.32 65	97.43 136	92.10 74	91.53 150	96.38 183	83.29 156	99.68 78	93.42 124	96.37 132	98.25 167

hse-mvs3		92.47 146	91.95 142	94.05 178	97.13 136	85.01 254	98.36 176	98.08 37	93.85 36	96.27 78	96.73 172	83.19 159	99.43 114	95.81 77	68.09 327	97.70 180

hse-mvs2		91.67 158	91.51 151	92.15 217	96.22 166	82.61 286	97.74 219	97.53 114	93.85 36	96.27 78	96.15 185	83.19 159	97.44 212	95.81 77	66.86 332	96.40 210

AUN-MVS		90.17 185	89.50 178	92.19 215	96.21 167	82.67 284	97.76 218	97.53 114	88.05 180	91.67 145	96.15 185	83.10 161	97.47 209	88.11 180	66.91 331	96.43 209

IS-MVSNet		93.00 135	92.51 129	94.49 162	96.14 173	87.36 200	98.31 181	95.70 256	88.58 160	90.17 171	97.50 141	83.02 162	97.22 217	87.06 188	96.07 141	98.90 128

tpm cat1		88.89 204	87.27 217	93.76 187	95.79 180	85.32 248	90.76 332	97.09 170	76.14 322	85.72 208	88.59 315	82.92 163	98.04 175	76.96 278	91.43 192	97.90 178

UniMVSNet_NR-MVSNet		89.60 194	88.55 200	92.75 206	92.17 272	90.07 142	98.74 125	98.15 34	88.37 171	83.21 228	93.98 217	82.86 164	95.93 280	86.95 191	72.47 311	92.25 236

cl_fuxian		88.19 221	87.23 218	91.06 236	94.97 214	86.17 229	97.72 220	95.38 278	83.43 261	81.68 260	91.37 266	82.81 165	95.72 290	84.04 227	73.70 299	91.29 274

TAPA-MVS	87.50 9	90.35 179	89.05 187	94.25 171	98.48 100	85.17 251	98.42 166	96.58 195	82.44 280	87.24 197	98.53 102	82.77 166	98.84 144	59.09 345	97.88 109	98.72 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
KD-MVS_2432*1600		82.98 286	80.52 294	90.38 254	94.32 229	88.98 166	92.87 316	95.87 247	80.46 301	73.79 317	87.49 321	82.76 167	93.29 325	70.56 316	46.53 353	88.87 322

miper_refine_blended		82.98 286	80.52 294	90.38 254	94.32 229	88.98 166	92.87 316	95.87 247	80.46 301	73.79 317	87.49 321	82.76 167	93.29 325	70.56 316	46.53 353	88.87 322

CANet		97.00 27	96.49 37	98.55 9	98.86 88	96.10 13	99.83 5	97.52 117	95.90 9	97.21 53	98.90 76	82.66 169	99.93 35	98.71 16	98.80 94	99.63 69

CPTT-MVS		94.60 96	94.43 86	95.09 143	99.66 15	86.85 212	99.44 48	97.47 128	83.22 264	94.34 115	98.96 68	82.50 170	99.55 94	94.81 99	99.50 58	98.88 129

mvs_anonymous		92.50 145	91.65 148	95.06 145	96.60 154	89.64 156	97.06 245	96.44 205	86.64 213	84.14 220	93.93 218	82.49 171	96.17 270	91.47 140	96.08 140	99.35 92

pcd_1.5k_mvsjas		6.87 336	9.16 339	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	82.48 172	0.00 366	0.00 364	0.00 364	0.00 362

PS-MVSNAJss		89.54 196	89.05 187	91.00 238	88.77 314	84.36 262	97.39 229	95.97 230	88.47 162	81.88 255	93.80 222	82.48 172	96.50 245	89.34 166	83.34 242	92.15 241

PS-MVSNAJ		96.87 33	96.40 39	98.29 15	97.35 128	97.29 3	99.03 96	97.11 166	95.83 10	98.97 11	99.14 43	82.48 172	99.60 91	98.60 19	99.08 80	98.00 175

UA-Net		93.30 128	92.62 127	95.34 138	96.27 164	88.53 179	95.88 286	96.97 179	90.90 97	95.37 98	97.07 159	82.38 175	99.10 138	83.91 228	94.86 153	98.38 160

FIs		90.70 176	89.87 175	93.18 195	92.29 269	91.12 113	98.17 192	98.25 26	89.11 144	83.44 226	94.82 205	82.26 176	96.17 270	87.76 183	82.76 245	92.25 236

ACMMP		94.67 93	94.30 87	95.79 124	99.25 64	88.13 183	98.41 168	98.67 19	90.38 107	91.43 151	98.72 89	82.22 177	99.95 30	93.83 116	95.76 145	99.29 98
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
xiu_mvs_v2_base		96.66 37	96.17 47	98.11 24	97.11 138	96.96 4	99.01 99	97.04 173	95.51 16	98.86 13	99.11 50	82.19 178	99.36 121	98.59 21	98.14 107	98.00 175

cl-mvsnet1		87.82 223	86.81 224	90.87 243	94.87 219	85.39 247	97.81 213	95.22 290	82.92 272	80.76 266	91.31 268	81.99 179	95.81 288	81.36 250	75.04 284	91.42 268

miper_lstm_enhance		86.90 237	86.20 233	89.00 287	94.53 226	81.19 299	96.74 259	95.24 286	82.33 281	80.15 274	90.51 293	81.99 179	94.68 315	80.71 256	73.58 301	91.12 278

cl-mvsnet_		87.82 223	86.79 225	90.89 242	94.88 218	85.43 246	97.81 213	95.24 286	82.91 273	80.71 267	91.22 269	81.97 181	95.84 286	81.34 251	75.06 283	91.40 269

FC-MVSNet-test		90.22 183	89.40 181	92.67 209	91.78 279	89.86 150	97.89 208	98.22 28	88.81 155	82.96 233	94.66 207	81.90 182	95.96 278	85.89 204	82.52 248	92.20 240

UniMVSNet (Re)		89.50 197	88.32 203	93.03 197	92.21 271	90.96 121	98.90 110	98.39 22	89.13 143	83.22 227	92.03 252	81.69 183	96.34 261	86.79 194	72.53 310	91.81 251

MVS_Test		93.67 118	92.67 126	96.69 89	96.72 151	92.66 84	97.22 240	96.03 228	87.69 194	95.12 103	94.03 214	81.55 184	98.28 161	89.17 170	96.46 129	99.14 110

sss		94.85 86	93.94 102	97.58 40	96.43 159	94.09 56	98.93 106	99.16 8	89.50 136	95.27 99	97.85 123	81.50 185	99.65 84	92.79 133	94.02 159	98.99 118

eth_miper_zixun_eth		87.76 225	87.00 222	90.06 261	94.67 224	82.65 285	97.02 248	95.37 279	84.19 249	81.86 258	91.58 263	81.47 186	95.90 284	83.24 232	73.61 300	91.61 259

jason		95.40 76	94.86 79	97.03 61	92.91 264	94.23 52	99.70 17	96.30 212	93.56 44	96.73 71	98.52 103	81.46 187	97.91 179	96.08 73	98.47 103	98.96 121
jason: jason.
IterMVS-LS		88.34 217	87.44 213	91.04 237	94.10 233	85.85 239	98.10 198	95.48 270	85.12 233	82.03 251	91.21 270	81.35 188	95.63 293	83.86 229	75.73 280	91.63 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		89.87 191	89.38 182	91.36 232	94.32 229	85.87 238	97.61 225	96.59 192	85.10 234	85.51 210	97.10 157	81.30 189	96.56 241	83.85 230	83.03 243	91.64 254

RPMNet		85.07 266	81.88 283	94.64 158	93.47 252	86.24 225	84.97 344	97.21 154	64.85 350	90.76 161	78.80 347	80.95 190	99.27 129	53.76 350	92.17 181	98.41 157

114514_t		94.06 104	93.05 118	97.06 60	99.08 76	92.26 93	98.97 103	97.01 177	82.58 276	92.57 137	98.22 117	80.68 191	99.30 128	89.34 166	99.02 83	99.63 69

CNLPA		93.64 119	92.74 124	96.36 105	98.96 82	90.01 148	99.19 70	95.89 245	86.22 220	89.40 180	98.85 79	80.66 192	99.84 55	88.57 174	96.92 125	99.24 103

diffmvs		94.59 97	94.19 90	95.81 123	95.54 189	90.69 127	98.70 130	95.68 258	91.61 80	95.96 84	97.81 125	80.11 193	98.06 173	96.52 63	95.76 145	98.67 147

abl_6		94.63 95	94.48 84	95.09 143	98.61 96	86.96 210	98.06 202	96.97 179	89.31 139	95.86 89	98.56 101	79.82 194	99.64 86	94.53 107	98.65 99	98.66 149

casdiffmvs		93.98 107	93.43 110	95.61 130	95.07 210	89.86 150	98.80 119	95.84 250	90.98 94	92.74 136	97.66 135	79.71 195	98.10 168	94.72 102	95.37 149	98.87 131

Effi-MVS+		93.87 111	93.15 116	96.02 115	95.79 180	90.76 125	96.70 261	95.78 251	86.98 205	95.71 92	97.17 155	79.58 196	98.01 177	94.57 106	96.09 139	99.31 96

baseline		93.91 109	93.30 112	95.72 126	95.10 209	90.07 142	97.48 228	95.91 242	91.03 93	93.54 127	97.68 133	79.58 196	98.02 176	94.27 110	95.14 150	99.08 114

canonicalmvs		95.02 83	93.96 100	98.20 17	97.53 125	95.92 14	98.71 126	96.19 222	91.78 78	95.86 89	98.49 107	79.53 198	99.03 140	96.12 71	91.42 193	99.66 65

OMC-MVS		93.90 110	93.62 108	94.73 155	98.63 94	87.00 209	98.04 203	96.56 196	92.19 71	92.46 138	98.73 87	79.49 199	99.14 136	92.16 137	94.34 157	98.03 174

MVS		93.92 108	92.28 132	98.83 4	95.69 184	96.82 5	96.22 276	98.17 31	84.89 241	84.34 219	98.61 99	79.32 200	99.83 57	93.88 114	99.43 64	99.86 28

VNet		95.08 82	94.26 88	97.55 43	98.07 109	93.88 58	98.68 133	98.73 15	90.33 109	97.16 55	97.43 144	79.19 201	99.53 97	96.91 56	91.85 185	99.24 103

CHOSEN 1792x2688		94.35 101	93.82 105	95.95 119	97.40 126	88.74 174	98.41 168	98.27 25	92.18 72	91.43 151	96.40 180	78.88 202	99.81 62	93.59 120	97.81 110	99.30 97

ADS-MVSNet2		87.62 230	86.88 223	89.86 266	96.21 167	79.14 311	87.15 337	92.99 324	83.01 267	89.91 175	87.27 324	78.87 203	92.80 331	74.20 299	92.27 178	97.64 181

ADS-MVSNet		88.99 201	87.30 216	94.07 176	96.21 167	87.56 194	87.15 337	96.78 185	83.01 267	89.91 175	87.27 324	78.87 203	97.01 224	74.20 299	92.27 178	97.64 181

nrg030		90.23 182	88.87 190	94.32 168	91.53 282	93.54 65	98.79 123	95.89 245	88.12 179	84.55 217	94.61 208	78.80 205	96.88 228	92.35 136	75.21 282	92.53 230

F-COLMAP		92.07 152	91.75 147	93.02 198	98.16 107	82.89 280	98.79 123	95.97 230	86.54 216	87.92 190	97.80 126	78.69 206	99.65 84	85.97 200	95.93 143	96.53 208

MAR-MVS		94.43 99	94.09 94	95.45 134	99.10 75	87.47 196	98.39 174	97.79 62	88.37 171	94.02 120	99.17 37	78.64 207	99.91 38	92.48 134	98.85 90	98.96 121
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PCF-MVS	89.78 5	91.26 164	89.63 177	96.16 112	95.44 192	91.58 103	95.29 295	96.10 226	85.07 236	82.75 234	97.45 143	78.28 208	99.78 66	80.60 257	95.65 148	97.12 194
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DeepC-MVS	91.02 4	94.56 98	93.92 103	96.46 99	97.16 134	90.76 125	98.39 174	97.11 166	93.92 31	88.66 185	98.33 112	78.14 209	99.85 54	95.02 95	98.57 100	98.78 141
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS_H		86.53 246	85.49 244	89.66 274	91.04 288	83.31 274	97.53 227	98.20 30	84.95 240	79.64 280	90.90 275	78.01 210	95.33 300	76.29 285	72.81 306	90.35 299

Fast-Effi-MVS+		91.72 157	90.79 165	94.49 162	95.89 178	87.40 199	99.54 35	95.70 256	85.01 239	89.28 182	95.68 193	77.75 211	97.57 207	83.22 233	95.06 151	98.51 153

1314		93.44 122	91.98 141	97.84 30	95.24 196	94.38 50	96.22 276	97.92 47	90.18 113	82.28 244	97.71 132	77.63 212	99.80 64	91.94 138	98.67 98	99.34 94

NR-MVSNet		87.74 228	86.00 236	92.96 199	91.46 283	90.68 128	96.65 262	97.42 137	88.02 182	73.42 319	93.68 224	77.31 213	95.83 287	84.26 220	71.82 318	92.36 233

BH-w/o		92.32 147	91.79 145	93.91 183	96.85 145	86.18 228	99.11 89	95.74 254	88.13 178	84.81 214	97.00 162	77.26 214	97.91 179	89.16 171	98.03 108	97.64 181

PMMVS		93.62 120	93.90 104	92.79 203	96.79 149	81.40 294	98.85 114	96.81 183	91.25 90	96.82 68	98.15 121	77.02 215	98.13 166	93.15 128	96.30 135	98.83 135

CVMVSNet		90.30 181	90.91 160	88.46 294	94.32 229	73.58 334	97.61 225	97.59 102	90.16 116	88.43 188	97.10 157	76.83 216	92.86 328	82.64 240	93.54 162	98.93 126

LCM-MVSNet-Re		88.59 214	88.61 197	88.51 293	95.53 190	72.68 338	96.85 253	88.43 353	88.45 165	73.14 321	90.63 285	75.82 217	94.38 318	92.95 129	95.71 147	98.48 155

LS3D		90.19 184	88.72 194	94.59 160	98.97 80	86.33 224	96.90 251	96.60 190	74.96 325	84.06 222	98.74 86	75.78 218	99.83 57	74.93 293	97.57 115	97.62 184

pmmvs4		87.58 231	86.17 234	91.80 224	89.58 304	88.92 169	97.25 237	95.28 282	82.54 277	80.49 269	93.17 238	75.62 219	96.05 275	82.75 239	78.90 262	90.42 298

BH-untuned		91.46 162	90.84 162	93.33 193	96.51 158	84.83 257	98.84 116	95.50 269	86.44 219	83.50 225	96.70 173	75.49 220	97.77 190	86.78 195	97.81 110	97.40 187

AdaColmap		93.82 112	93.06 117	96.10 113	99.88 1	89.07 163	98.33 178	97.55 110	86.81 210	90.39 169	98.65 95	75.09 221	99.98 10	93.32 125	97.53 118	99.26 102

bset_n11_16_dypcd		89.07 200	87.85 207	92.76 205	86.16 335	90.66 129	97.30 233	95.62 261	89.78 125	83.94 223	93.15 240	74.85 222	95.89 285	91.34 142	78.48 264	91.74 252

DU-MVS		88.83 208	87.51 212	92.79 203	91.46 283	90.07 142	98.71 126	97.62 95	88.87 154	83.21 228	93.68 224	74.63 223	95.93 280	86.95 191	72.47 311	92.36 233

Baseline_NR-MVSNet		85.83 256	84.82 254	88.87 290	88.73 315	83.34 273	98.63 141	91.66 340	80.41 303	82.44 240	91.35 267	74.63 223	95.42 298	84.13 223	71.39 320	87.84 327

v148		86.38 248	85.06 248	90.37 256	89.47 308	84.10 265	98.52 153	95.48 270	83.80 254	80.93 265	90.22 300	74.60 225	96.31 263	80.92 254	71.55 319	90.69 293

3Dnovator+	87.72 8	93.43 123	91.84 144	98.17 18	95.73 183	95.08 29	98.92 108	97.04 173	91.42 87	81.48 262	97.60 137	74.60 225	99.79 65	90.84 149	98.97 85	99.64 67

test_part1		88.43 216	86.68 226	93.67 189	97.56 124	92.40 92	98.12 195	96.55 197	82.26 282	80.31 271	93.16 239	74.59 227	96.62 238	85.00 212	72.61 309	91.99 248

v8		86.11 251	84.45 260	91.10 235	89.99 297	86.85 212	97.24 238	95.36 280	81.99 285	79.89 278	89.86 305	74.53 228	96.39 253	78.83 268	72.32 313	90.05 307

DP-MVS		88.75 212	86.56 228	95.34 138	98.92 84	87.45 197	97.64 224	93.52 321	70.55 336	81.49 261	97.25 148	74.43 229	99.88 44	71.14 314	94.09 158	98.67 147

cdsmvs_eth3d_5k		22.52 331	30.03 334	0.00 348	0.00 369	0.00 370	0.00 360	97.17 160	0.00 365	0.00 366	98.77 83	74.35 230	0.00 366	0.00 364	0.00 364	0.00 362

Effi-MVS+-dtu		89.97 190	90.68 167	87.81 298	95.15 203	71.98 340	97.87 211	95.40 276	91.92 75	87.57 192	91.44 265	74.27 231	96.84 229	89.45 162	93.10 165	94.60 219

mvs-test1		91.57 159	92.20 135	89.70 271	95.15 203	74.34 330	99.51 37	95.40 276	91.92 75	91.02 157	97.25 148	74.27 231	98.08 172	89.45 162	95.83 144	96.67 202

WR-MVS		88.54 215	87.22 219	92.52 210	91.93 277	89.50 158	98.56 151	97.84 53	86.99 203	81.87 256	93.81 221	74.25 233	95.92 282	85.29 207	74.43 291	92.12 242

FMVSNet3		88.81 210	87.08 220	93.99 181	96.52 157	94.59 45	98.08 200	96.20 220	85.85 223	82.12 247	91.60 262	74.05 234	95.40 299	79.04 264	80.24 255	91.99 248

V42		87.00 236	85.68 241	90.98 239	89.91 298	86.08 232	98.32 180	95.61 263	83.67 258	82.72 235	90.67 282	74.00 235	96.53 243	81.94 248	74.28 294	90.32 300

D2MVS		87.96 222	87.39 214	89.70 271	91.84 278	83.40 272	98.31 181	98.49 20	88.04 181	78.23 297	90.26 296	73.57 236	96.79 233	84.21 221	83.53 239	88.90 321

v1144		86.83 239	85.31 246	91.40 230	89.75 301	87.21 208	98.31 181	95.45 272	83.22 264	82.70 236	90.78 277	73.36 237	96.36 255	79.49 261	74.69 288	90.63 295

HQP2-MVS												73.34 238

HQP-MVS		91.50 160	91.23 154	92.29 212	93.95 237	86.39 221	99.16 75	96.37 208	93.92 31	87.57 192	96.67 174	73.34 238	97.77 190	93.82 117	86.29 216	92.72 226

v10		85.73 260	84.01 266	90.87 243	90.03 296	86.73 214	97.20 241	95.22 290	81.25 293	79.85 279	89.75 306	73.30 240	96.28 267	76.87 279	72.64 308	89.61 314

v2v482		87.27 234	85.76 239	91.78 228	89.59 303	87.58 193	98.56 151	95.54 267	84.53 245	82.51 239	91.78 259	73.11 241	96.47 248	82.07 245	74.14 297	91.30 273

HQP_MVS		91.26 164	90.95 159	92.16 216	93.84 244	86.07 233	99.02 97	96.30 212	93.38 46	86.99 198	96.52 176	72.92 242	97.75 195	93.46 122	86.17 219	92.67 228

plane_prior6					93.92 241	86.02 235						72.92 242

QAPM		91.41 163	89.49 179	97.17 58	95.66 186	93.42 68	98.60 146	97.51 120	80.92 298	81.39 263	97.41 145	72.89 244	99.87 47	82.33 243	98.68 97	98.21 170

v144192		86.40 247	84.89 252	90.91 240	89.48 307	85.59 243	98.21 188	95.43 275	82.45 279	82.62 237	90.58 289	72.79 245	96.36 255	78.45 270	74.04 298	90.79 287

TranMVSNet+NR-MVSNet		87.75 226	86.31 231	92.07 219	90.81 290	88.56 176	98.33 178	97.18 159	87.76 189	81.87 256	93.90 219	72.45 246	95.43 297	83.13 236	71.30 321	92.23 238

xiu_mvs_v1_base_debu		94.73 89	93.98 97	96.99 65	95.19 199	95.24 21	98.62 142	96.50 201	92.99 50	97.52 47	98.83 80	72.37 247	99.15 133	97.03 50	96.74 126	96.58 205

xiu_mvs_v1_base		94.73 89	93.98 97	96.99 65	95.19 199	95.24 21	98.62 142	96.50 201	92.99 50	97.52 47	98.83 80	72.37 247	99.15 133	97.03 50	96.74 126	96.58 205

xiu_mvs_v1_base_debi		94.73 89	93.98 97	96.99 65	95.19 199	95.24 21	98.62 142	96.50 201	92.99 50	97.52 47	98.83 80	72.37 247	99.15 133	97.03 50	96.74 126	96.58 205

test_djsdf		88.26 220	87.73 209	89.84 267	88.05 323	82.21 288	97.77 216	96.17 223	86.84 208	82.41 242	91.95 257	72.07 250	95.99 276	89.83 157	84.50 230	91.32 272

3Dnovator	87.35 11	93.17 133	91.77 146	97.37 51	95.41 193	93.07 76	98.82 117	97.85 52	91.53 82	82.56 238	97.58 139	71.97 251	99.82 60	91.01 146	99.23 78	99.22 106

CANet_DTU		94.31 102	93.35 111	97.20 57	97.03 142	94.71 42	98.62 142	95.54 267	95.61 14	97.21 53	98.47 109	71.88 252	99.84 55	88.38 176	97.46 120	97.04 199

CP-MVSNet		86.54 245	85.45 245	89.79 269	91.02 289	82.78 283	97.38 231	97.56 109	85.37 230	79.53 283	93.03 242	71.86 253	95.25 302	79.92 259	73.43 304	91.34 271

PatchMatch-RL		91.47 161	90.54 169	94.26 170	98.20 104	86.36 223	96.94 249	97.14 162	87.75 190	88.98 183	95.75 192	71.80 254	99.40 118	80.92 254	97.39 121	97.02 200

our_test_3		84.47 275	82.80 274	89.50 277	89.01 311	83.90 268	97.03 246	94.56 301	81.33 292	75.36 311	90.52 292	71.69 255	94.54 317	68.81 320	76.84 277	90.07 305

XVG-OURS		90.83 173	90.49 170	91.86 221	95.23 197	81.25 298	95.79 291	95.92 238	88.96 149	90.02 174	98.03 122	71.60 256	99.35 125	91.06 145	87.78 211	94.98 217

v1192		86.32 249	84.71 256	91.17 234	89.53 306	86.40 220	98.13 193	95.44 274	82.52 278	82.42 241	90.62 286	71.58 257	96.33 262	77.23 275	74.88 285	90.79 287

Vis-MVSNet		92.64 140	91.85 143	95.03 147	95.12 205	88.23 181	98.48 161	96.81 183	91.61 80	92.16 142	97.22 151	71.58 257	98.00 178	85.85 206	97.81 110	98.88 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PVSNet	87.13 12	93.69 115	92.83 123	96.28 107	97.99 111	90.22 137	99.38 56	98.93 10	91.42 87	93.66 126	97.68 133	71.29 259	99.64 86	87.94 182	97.20 123	98.98 119

v1921920		86.02 252	84.44 261	90.77 245	89.32 309	85.20 249	98.10 198	95.35 281	82.19 283	82.25 245	90.71 279	70.73 260	96.30 266	76.85 280	74.49 290	90.80 286

EU-MVSNet		84.19 278	84.42 262	83.52 323	88.64 317	67.37 349	96.04 282	95.76 253	85.29 231	78.44 294	93.18 237	70.67 261	91.48 344	75.79 289	75.98 278	91.70 253

XVG-OURS-SEG-HR		90.95 171	90.66 168	91.83 222	95.18 202	81.14 301	95.92 283	95.92 238	88.40 170	90.33 170	97.85 123	70.66 262	99.38 119	92.83 132	88.83 207	94.98 217

v7n		84.42 276	82.75 277	89.43 280	88.15 321	81.86 290	96.75 258	95.67 259	80.53 299	78.38 295	89.43 310	69.89 263	96.35 260	73.83 303	72.13 315	90.07 305

ppachtmachnet_test		83.63 284	81.57 287	89.80 268	89.01 311	85.09 253	97.13 243	94.50 302	78.84 308	76.14 303	91.00 273	69.78 264	94.61 316	63.40 335	74.36 292	89.71 313

MSDG		88.29 219	86.37 230	94.04 179	96.90 144	86.15 230	96.52 265	94.36 307	77.89 316	79.22 286	96.95 164	69.72 265	99.59 92	73.20 307	92.58 173	96.37 211

CLD-MVS		91.06 168	90.71 166	92.10 218	94.05 236	86.10 231	99.55 33	96.29 215	94.16 26	84.70 215	97.17 155	69.62 266	97.82 186	94.74 101	86.08 221	92.39 231
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1240		85.77 259	84.11 264	90.73 246	89.26 310	85.15 252	97.88 210	95.23 289	81.89 288	82.16 246	90.55 291	69.60 267	96.31 263	75.59 290	74.87 286	90.72 292

Fast-Effi-MVS+-dtu		88.84 206	88.59 199	89.58 275	93.44 255	78.18 318	98.65 137	94.62 300	88.46 164	84.12 221	95.37 199	68.91 268	96.52 244	82.06 246	91.70 189	94.06 220

anonymousdsp		86.69 241	85.75 240	89.53 276	86.46 334	82.94 277	96.39 267	95.71 255	83.97 253	79.63 281	90.70 280	68.85 269	95.94 279	86.01 199	84.02 234	89.72 312

VPA-MVSNet		89.10 199	87.66 211	93.45 191	92.56 266	91.02 119	97.97 206	98.32 24	86.92 207	86.03 207	92.01 254	68.84 270	97.10 222	90.92 147	75.34 281	92.23 238

ab-mvs		91.05 169	89.17 185	96.69 89	95.96 177	91.72 98	92.62 319	97.23 152	85.61 226	89.74 177	93.89 220	68.55 271	99.42 115	91.09 144	87.84 210	98.92 127

CL-MVSNet_2432*1600		79.89 301	78.34 301	84.54 319	81.56 347	75.01 327	96.88 252	95.62 261	81.10 294	75.86 307	85.81 334	68.49 272	90.26 346	63.21 336	56.51 346	88.35 324

PEN-MVS		85.21 265	83.93 267	89.07 286	89.89 300	81.31 297	97.09 244	97.24 150	84.45 247	78.66 290	92.68 247	68.44 273	94.87 309	75.98 287	70.92 322	91.04 280

BH-RMVSNet		91.25 166	89.99 174	95.03 147	96.75 150	88.55 177	98.65 137	94.95 292	87.74 191	87.74 191	97.80 126	68.27 274	98.14 165	80.53 258	97.49 119	98.41 157

GA-MVS		90.10 187	88.69 195	94.33 167	92.44 268	87.97 187	99.08 90	96.26 216	89.65 128	86.92 200	93.11 241	68.09 275	96.96 225	82.54 242	90.15 203	98.05 173

MDA-MVSNet_test_wron		79.65 302	77.05 306	87.45 301	87.79 327	80.13 307	96.25 274	94.44 303	73.87 329	51.80 351	87.47 323	68.04 276	92.12 340	66.02 329	67.79 329	90.09 303

OpenMVS	85.28 14	90.75 175	88.84 191	96.48 98	93.58 250	93.51 66	98.80 119	97.41 138	82.59 275	78.62 291	97.49 142	68.00 277	99.82 60	84.52 218	98.55 101	96.11 213

YYNet1		79.64 303	77.04 307	87.43 302	87.80 326	79.98 308	96.23 275	94.44 303	73.83 330	51.83 350	87.53 320	67.96 278	92.07 341	66.00 330	67.75 330	90.23 302

DTE-MVSNet		84.14 279	82.80 274	88.14 295	88.95 313	79.87 309	96.81 254	96.24 217	83.50 260	77.60 299	92.52 249	67.89 279	94.24 320	72.64 310	69.05 325	90.32 300

MVP-Stereo		86.61 244	85.83 238	88.93 289	88.70 316	83.85 269	96.07 281	94.41 306	82.15 284	75.64 309	91.96 256	67.65 280	96.45 250	77.20 277	98.72 96	86.51 338
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XXY-MVS		87.75 226	86.02 235	92.95 200	90.46 294	89.70 155	97.71 222	95.90 243	84.02 251	80.95 264	94.05 211	67.51 281	97.10 222	85.16 208	78.41 265	92.04 247

PS-CasMVS		85.81 257	84.58 259	89.49 279	90.77 291	82.11 289	97.20 241	97.36 144	84.83 242	79.12 288	92.84 245	67.42 282	95.16 304	78.39 271	73.25 305	91.21 276

ACMM	86.95 13	88.77 211	88.22 205	90.43 252	93.61 249	81.34 296	98.50 158	95.92 238	87.88 186	83.85 224	95.20 200	67.20 283	97.89 181	86.90 193	84.90 227	92.06 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TransMVSNet (Re)		81.97 291	79.61 299	89.08 285	89.70 302	84.01 266	97.26 236	91.85 339	78.84 308	73.07 324	91.62 261	67.17 284	95.21 303	67.50 324	59.46 343	88.02 326

OPM-MVS		89.76 192	89.15 186	91.57 229	90.53 293	85.58 244	98.11 197	95.93 237	92.88 55	86.05 206	96.47 179	67.06 285	97.87 183	89.29 169	86.08 221	91.26 275

TR-MVS		90.77 174	89.44 180	94.76 152	96.31 163	88.02 186	97.92 207	95.96 232	85.52 227	88.22 189	97.23 150	66.80 286	98.09 169	84.58 217	92.38 175	98.17 172

IterMVS-SCA-FT		85.73 260	84.64 258	89.00 287	93.46 254	82.90 279	96.27 271	94.70 297	85.02 238	78.62 291	90.35 295	66.61 287	93.33 324	79.38 263	77.36 275	90.76 289

SCA		90.64 178	89.25 184	94.83 151	94.95 215	88.83 170	96.26 273	97.21 154	90.06 120	90.03 173	90.62 286	66.61 287	96.81 231	83.16 234	94.36 156	98.84 132

IterMVS		85.81 257	84.67 257	89.22 282	93.51 251	83.67 270	96.32 270	94.80 294	85.09 235	78.69 289	90.17 303	66.57 289	93.17 327	79.48 262	77.42 274	90.81 285
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LPG-MVS_test		88.86 205	88.47 202	90.06 261	93.35 257	80.95 303	98.22 186	95.94 235	87.73 192	83.17 230	96.11 187	66.28 290	97.77 190	90.19 155	85.19 225	91.46 265

LGP-MVS_train				90.06 261	93.35 257	80.95 303		95.94 235	87.73 192	83.17 230	96.11 187	66.28 290	97.77 190	90.19 155	85.19 225	91.46 265

ACMP	87.39 10	88.71 213	88.24 204	90.12 260	93.91 242	81.06 302	98.50 158	95.67 259	89.43 137	80.37 270	95.55 194	65.67 292	97.83 185	90.55 152	84.51 229	91.47 264
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	81.71 19	84.59 272	82.72 278	90.18 258	92.89 265	83.18 275	93.15 313	94.74 295	78.99 307	75.14 312	92.69 246	65.64 293	97.63 201	69.46 318	81.82 252	89.74 311
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
pm-mvs1		84.68 270	82.78 276	90.40 253	89.58 304	85.18 250	97.31 232	94.73 296	81.93 287	76.05 304	92.01 254	65.48 294	96.11 273	78.75 269	69.14 324	89.91 310

cascas		90.93 172	89.33 183	95.76 125	95.69 184	93.03 78	98.99 101	96.59 192	80.49 300	86.79 204	94.45 209	65.23 295	98.60 155	93.52 121	92.18 180	95.66 216

tfpnnormal		83.65 283	81.35 289	90.56 249	91.37 285	88.06 184	97.29 234	97.87 51	78.51 311	76.20 302	90.91 274	64.78 296	96.47 248	61.71 340	73.50 302	87.13 335

pmmvs5		85.87 254	84.40 263	90.30 257	88.53 318	84.23 263	98.60 146	93.71 317	81.53 290	80.29 272	92.02 253	64.51 297	95.52 295	82.04 247	78.34 266	91.15 277

RPSCF		85.33 264	85.55 243	84.67 318	94.63 225	62.28 351	93.73 308	93.76 315	74.38 328	85.23 213	97.06 160	64.09 298	98.31 158	80.98 252	86.08 221	93.41 225

N_pmnet		70.19 320	69.87 322	71.12 335	88.24 320	30.63 366	95.85 289	28.70 366	70.18 338	68.73 333	86.55 330	64.04 299	93.81 321	53.12 351	73.46 303	88.94 320

DSMNet-mixed		81.60 294	81.43 288	82.10 327	84.36 339	60.79 352	93.63 310	86.74 355	79.00 306	79.32 285	87.15 326	63.87 300	89.78 347	66.89 327	91.92 183	95.73 215

FMVSNet5		82.29 289	80.54 293	87.52 300	93.79 247	84.01 266	93.73 308	92.47 330	76.92 319	74.27 314	86.15 333	63.69 301	89.24 348	69.07 319	74.79 287	89.29 317

GBi-Net		86.67 242	84.96 249	91.80 224	95.11 206	88.81 171	96.77 255	95.25 283	82.94 269	82.12 247	90.25 297	62.89 302	94.97 306	79.04 264	80.24 255	91.62 256

test1		86.67 242	84.96 249	91.80 224	95.11 206	88.81 171	96.77 255	95.25 283	82.94 269	82.12 247	90.25 297	62.89 302	94.97 306	79.04 264	80.24 255	91.62 256

FMVSNet2		86.90 237	84.79 255	93.24 194	95.11 206	92.54 90	97.67 223	95.86 249	82.94 269	80.55 268	91.17 271	62.89 302	95.29 301	77.23 275	79.71 261	91.90 250

VPNet		88.30 218	86.57 227	93.49 190	91.95 275	91.35 106	98.18 190	97.20 158	88.61 158	84.52 218	94.89 203	62.21 305	96.76 234	89.34 166	72.26 314	92.36 233

PVSNet_0	83.28 16	87.31 233	85.16 247	93.74 188	94.78 221	84.59 259	98.91 109	98.69 18	89.81 124	78.59 293	93.23 236	61.95 306	99.34 126	94.75 100	55.72 348	97.30 190

jajsoiax		87.35 232	86.51 229	89.87 265	87.75 328	81.74 291	97.03 246	95.98 229	88.47 162	80.15 274	93.80 222	61.47 307	96.36 255	89.44 164	84.47 231	91.50 263

OurMVSNet-221017-0		84.13 280	83.59 269	85.77 312	87.81 325	70.24 343	94.89 298	93.65 319	86.08 221	76.53 301	93.28 235	61.41 308	96.14 272	80.95 253	77.69 273	90.93 282

Anonymous20231206		80.76 296	79.42 300	84.79 317	84.78 338	72.98 335	96.53 264	92.97 325	79.56 304	74.33 313	88.83 313	61.27 309	92.15 339	60.59 342	75.92 279	89.24 318

LFMVS		92.23 150	90.84 162	96.42 102	98.24 103	91.08 117	98.24 185	96.22 219	83.39 262	94.74 108	98.31 113	61.12 310	98.85 143	94.45 108	92.82 167	99.32 95

UGNet		91.91 155	90.85 161	95.10 142	97.06 140	88.69 175	98.01 204	98.24 27	92.41 67	92.39 140	93.61 227	60.52 311	99.68 78	88.14 179	97.25 122	96.92 201
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
SixPastTwentyTwo		82.63 288	81.58 286	85.79 311	88.12 322	71.01 342	95.17 296	92.54 329	84.33 248	72.93 325	92.08 251	60.41 312	95.61 294	74.47 297	74.15 296	90.75 290

mvs_tets		87.09 235	86.22 232	89.71 270	87.87 324	81.39 295	96.73 260	95.90 243	88.19 177	79.99 276	93.61 227	59.96 313	96.31 263	89.40 165	84.34 232	91.43 267

COLMAP_ROB	82.69 18	84.54 273	82.82 273	89.70 271	96.72 151	78.85 312	95.89 284	92.83 327	71.55 334	77.54 300	95.89 191	59.40 314	99.14 136	67.26 325	88.26 208	91.11 279
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20231211		84.72 269	82.65 280	90.91 240	97.71 116	84.55 260	97.28 235	96.67 187	66.88 347	79.18 287	90.87 276	58.47 315	96.60 239	82.61 241	74.20 295	91.59 261

MS-PatchMatch		86.75 240	85.92 237	89.22 282	91.97 274	82.47 287	96.91 250	96.14 225	83.74 255	77.73 298	93.53 230	58.19 316	97.37 216	76.75 281	98.35 105	87.84 327

test20.03		78.51 309	77.48 304	81.62 329	83.07 343	71.03 341	96.11 280	92.83 327	81.66 289	69.31 332	89.68 307	57.53 317	87.29 352	58.65 346	68.47 326	86.53 337

MVS-HIRNet		79.01 304	75.13 314	90.66 247	93.82 246	81.69 292	85.16 341	93.75 316	54.54 352	74.17 315	59.15 355	57.46 318	96.58 240	63.74 334	94.38 155	93.72 222

MDA-MVSNet-bldmvs		77.82 312	74.75 316	87.03 304	88.33 319	78.52 316	96.34 269	92.85 326	75.57 323	48.87 353	87.89 317	57.32 319	92.49 336	60.79 341	64.80 336	90.08 304

ACMH	83.09 17	84.60 271	82.61 281	90.57 248	93.18 260	82.94 277	96.27 271	94.92 293	81.01 296	72.61 327	93.61 227	56.54 320	97.79 188	74.31 298	81.07 254	90.99 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ITE_SJBPF				87.93 296	92.26 270	76.44 324		93.47 322	87.67 195	79.95 277	95.49 197	56.50 321	97.38 214	75.24 291	82.33 249	89.98 309

pmmvs-eth3d		78.71 307	76.16 311	86.38 307	80.25 350	81.19 299	94.17 304	92.13 335	77.97 313	66.90 342	82.31 339	55.76 322	92.56 334	73.63 305	62.31 340	85.38 342

K. test v3		81.04 295	79.77 298	84.83 316	87.41 329	70.23 344	95.60 293	93.93 314	83.70 257	67.51 339	89.35 311	55.76 322	93.58 323	76.67 282	68.03 328	90.67 294

AllTest		84.97 267	83.12 271	90.52 250	96.82 146	78.84 313	95.89 284	92.17 333	77.96 314	75.94 305	95.50 195	55.48 324	99.18 131	71.15 312	87.14 212	93.55 223

TestCases				90.52 250	96.82 146	78.84 313		92.17 333	77.96 314	75.94 305	95.50 195	55.48 324	99.18 131	71.15 312	87.14 212	93.55 223

DIV-MVS_2432*1600		77.47 313	75.88 312	82.24 325	81.59 346	68.93 347	92.83 318	94.02 313	77.03 318	73.14 321	83.39 337	55.44 326	90.42 345	67.95 323	57.53 345	87.38 330

CMPMVS	58.40 21	80.48 297	80.11 297	81.59 330	85.10 337	59.56 353	94.14 305	95.95 234	68.54 343	60.71 348	93.31 233	55.35 327	97.87 183	83.06 237	84.85 228	87.33 332
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20240529		87.66 229	85.58 242	93.92 182	97.59 122	85.01 254	98.13 193	97.13 164	66.69 348	88.47 187	96.01 190	55.09 328	99.51 101	87.00 190	84.12 233	97.23 193

VDDNet		90.08 188	88.54 201	94.69 156	94.41 228	87.68 190	98.21 188	96.40 206	76.21 321	93.33 129	97.75 129	54.93 329	98.77 146	94.71 103	90.96 196	97.61 185

ACMH+	83.78 15	84.21 277	82.56 282	89.15 284	93.73 248	79.16 310	96.43 266	94.28 308	81.09 295	74.00 316	94.03 214	54.58 330	97.67 198	76.10 286	78.81 263	90.63 295

VDD-MVS		91.24 167	90.18 173	94.45 164	97.08 139	85.84 240	98.40 171	96.10 226	86.99 203	93.36 128	98.16 120	54.27 331	99.20 130	96.59 61	90.63 201	98.31 166

lessismore_v0				85.08 314	85.59 336	69.28 346		90.56 347		67.68 338	90.21 301	54.21 332	95.46 296	73.88 301	62.64 338	90.50 297

USDC		84.74 268	82.93 272	90.16 259	91.73 280	83.54 271	95.00 297	93.30 323	88.77 156	73.19 320	93.30 234	53.62 333	97.65 200	75.88 288	81.54 253	89.30 316

Anonymous202405211		88.84 206	87.03 221	94.27 169	98.14 108	84.18 264	98.44 164	95.58 265	76.79 320	89.34 181	96.88 168	53.42 334	99.54 96	87.53 187	87.12 214	99.09 113

XVG-ACMP-BASELINE		85.86 255	84.95 251	88.57 291	89.90 299	77.12 323	94.30 302	95.60 264	87.40 200	82.12 247	92.99 244	53.42 334	97.66 199	85.02 211	83.83 235	90.92 283

test_0402		78.81 306	76.33 310	86.26 308	91.18 286	78.44 317	95.88 286	91.34 344	68.55 342	70.51 330	89.91 304	52.65 336	94.99 305	47.14 353	79.78 260	85.34 344

MIMVSNet		84.48 274	81.83 284	92.42 211	91.73 280	87.36 200	85.52 340	94.42 305	81.40 291	81.91 254	87.58 319	51.92 337	92.81 330	73.84 302	88.15 209	97.08 198

UnsupCasMVSNet_eth		78.90 305	76.67 309	85.58 313	82.81 345	74.94 328	91.98 322	96.31 211	84.64 244	65.84 344	87.71 318	51.33 338	92.23 338	72.89 309	56.50 347	89.56 315

new-patchmatchnet		74.80 317	72.40 320	81.99 328	78.36 353	72.20 339	94.44 300	92.36 331	77.06 317	63.47 345	79.98 345	51.04 339	88.85 349	60.53 343	54.35 349	84.92 345

pmmvs6		79.90 300	77.31 305	87.67 299	84.17 340	78.13 319	95.86 288	93.68 318	67.94 345	72.67 326	89.62 308	50.98 340	95.75 289	74.80 296	66.04 333	89.14 319

FMVSNet1		83.94 282	81.32 290	91.80 224	91.94 276	88.81 171	96.77 255	95.25 283	77.98 312	78.25 296	90.25 297	50.37 341	94.97 306	73.27 306	77.81 272	91.62 256

UniMVSNet_ETH3D		85.65 262	83.79 268	91.21 233	90.41 295	80.75 305	95.36 294	95.78 251	78.76 310	81.83 259	94.33 210	49.86 342	96.66 236	84.30 219	83.52 240	96.22 212

Anonymous20240521		78.63 308	76.90 308	83.82 321	82.82 344	72.86 336	95.72 292	93.57 320	73.55 331	72.17 328	84.79 336	49.69 343	92.51 335	65.29 332	74.50 289	86.09 340

TDRefinement		78.01 310	75.31 313	86.10 310	70.06 356	73.84 332	93.59 311	91.58 342	74.51 327	73.08 323	91.04 272	49.63 344	97.12 219	74.88 294	59.47 342	87.33 332

LF4IMVS		81.94 292	81.17 291	84.25 320	87.23 331	68.87 348	93.35 312	91.93 338	83.35 263	75.40 310	93.00 243	49.25 345	96.65 237	78.88 267	78.11 267	87.22 334

new_pmnet		76.02 314	73.71 317	82.95 324	83.88 341	72.85 337	91.26 328	92.26 332	70.44 337	62.60 346	81.37 341	47.64 346	92.32 337	61.85 339	72.10 316	83.68 347

TinyColmap		80.42 298	77.94 302	87.85 297	92.09 273	78.58 315	93.74 307	89.94 349	74.99 324	69.77 331	91.78 259	46.09 347	97.58 204	65.17 333	77.89 268	87.38 330

testgi		82.29 289	81.00 292	86.17 309	87.24 330	74.84 329	97.39 229	91.62 341	88.63 157	75.85 308	95.42 198	46.07 348	91.55 343	66.87 328	79.94 259	92.12 242

OpenMVS_ROB	73.86 20	77.99 311	75.06 315	86.77 306	83.81 342	77.94 321	96.38 268	91.53 343	67.54 346	68.38 334	87.13 327	43.94 349	96.08 274	55.03 349	81.83 251	86.29 339

tmp_tt		53.66 325	52.86 327	56.05 340	32.75 366	41.97 362	73.42 355	76.12 362	21.91 361	39.68 357	96.39 182	42.59 350	65.10 359	78.00 272	14.92 360	61.08 353

pmmvs3		72.86 319	69.76 323	82.17 326	73.86 354	74.19 331	94.20 303	89.01 352	64.23 351	67.72 337	80.91 343	41.48 351	88.65 350	62.40 338	54.02 350	83.68 347

UnsupCasMVSNet_bld		73.85 318	70.14 321	84.99 315	79.44 351	75.73 325	88.53 335	95.24 286	70.12 339	61.94 347	74.81 348	41.41 352	93.62 322	68.65 321	51.13 352	85.62 341

MVS_0304		84.13 280	82.66 279	88.52 292	93.07 262	80.15 306	95.81 290	98.21 29	79.27 305	86.85 202	86.40 331	41.33 353	94.69 314	76.36 284	86.69 215	90.73 291

MIMVSNet1		75.92 315	73.30 318	83.81 322	81.29 348	75.57 326	92.26 321	92.05 336	73.09 332	67.48 340	86.18 332	40.87 354	87.64 351	55.78 348	70.68 323	88.21 325

EG-PatchMatch MVS		79.92 299	77.59 303	86.90 305	87.06 332	77.90 322	96.20 279	94.06 312	74.61 326	66.53 343	88.76 314	40.40 355	96.20 268	67.02 326	83.66 238	86.61 336

DeepMVS_CX				76.08 333	90.74 292	51.65 358		90.84 346	86.47 218	57.89 349	87.98 316	35.88 356	92.60 332	65.77 331	65.06 335	83.97 346

PM-MVS		74.88 316	72.85 319	80.98 331	78.98 352	64.75 350	90.81 331	85.77 356	80.95 297	68.23 336	82.81 338	29.08 357	92.84 329	76.54 283	62.46 339	85.36 343

ambc				79.60 332	72.76 355	56.61 355	76.20 353	92.01 337		68.25 335	80.23 344	23.34 358	94.73 313	73.78 304	60.81 341	87.48 329

FPMVS		61.57 321	60.32 324	65.34 337	60.14 360	42.44 361	91.02 330	89.72 350	44.15 354	42.63 355	80.93 342	19.02 359	80.59 356	42.50 354	72.76 307	73.00 351

EMVS		39.96 330	39.88 332	40.18 344	59.57 361	32.12 365	84.79 346	64.57 365	26.27 359	26.14 360	44.18 362	18.73 360	59.29 362	17.03 360	17.67 359	29.12 358

Gipuma		54.77 324	52.22 328	62.40 339	86.50 333	59.37 354	50.20 358	90.35 348	36.52 356	41.20 356	49.49 357	18.33 361	81.29 354	32.10 356	65.34 334	46.54 356
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		41.02 329	40.93 331	41.29 343	61.97 358	33.83 363	84.00 349	65.17 364	27.17 358	27.56 358	46.72 359	17.63 362	60.41 361	19.32 359	18.82 357	29.61 357

PMMVS2		58.97 323	55.07 326	70.69 336	62.72 357	55.37 356	85.97 339	80.52 359	49.48 353	45.94 354	68.31 350	15.73 363	80.78 355	49.79 352	37.12 355	75.91 350

ANet_high		50.71 326	46.17 329	64.33 338	44.27 364	52.30 357	76.13 354	78.73 360	64.95 349	27.37 359	55.23 356	14.61 364	67.74 358	36.01 355	18.23 358	72.95 352

LCM-MVSNet		60.07 322	56.37 325	71.18 334	54.81 362	48.67 359	82.17 352	89.48 351	37.95 355	49.13 352	69.12 349	13.75 365	81.76 353	59.28 344	51.63 351	83.10 349

PMVS	41.42 23	45.67 327	42.50 330	55.17 341	34.28 365	32.37 364	66.24 356	78.71 361	30.72 357	22.04 362	59.59 354	4.59 366	77.85 357	27.49 357	58.84 344	55.29 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		16.71 333	16.73 337	16.65 345	60.15 359	25.22 367	41.24 359	5.17 367	6.56 362	5.48 365	3.61 365	3.64 367	22.72 363	15.20 361	9.52 361	1.99 361

MVE	44.00 22	41.70 328	37.64 333	53.90 342	49.46 363	43.37 360	65.09 357	66.66 363	26.19 360	25.77 361	48.53 358	3.58 368	63.35 360	26.15 358	27.28 356	54.97 355
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		16.58 334	19.47 336	7.91 346	3.59 368	5.37 368	94.32 301	1.39 369	2.49 364	13.98 364	44.60 361	2.91 369	2.65 364	11.35 363	0.57 363	15.70 359

testmvs		18.81 332	23.05 335	6.10 347	4.48 367	2.29 369	97.78 215	3.00 368	3.27 363	18.60 363	62.71 352	1.53 370	2.49 365	14.26 362	1.80 362	13.50 360

uanet_test		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

sosnet-low-res		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

sosnet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

uncertanet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

Regformer		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

ab-mvs-re		8.21 335	10.94 338	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	98.50 105	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

uanet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

IU-MVS					99.63 21	95.38 19		97.73 72	95.54 15	99.54 1				99.69 4	99.81 19	99.99 1

save fliter					99.34 53	93.85 59	99.65 23	97.63 93	95.69 11

test_0728_SECOND				98.77 5	99.66 15	96.37 11	99.72 14	97.68 81					99.98 10	99.64 5	99.82 15	99.96 8

GSMVS																98.84 132

test_part2					99.54 36	95.42 17				98.13 32

MTGPA								97.45 130

MTMP							99.21 69	91.09 345

gm-plane-assit					94.69 223	88.14 182			88.22 176		97.20 152		98.29 160	90.79 150

test9_res														98.60 19	99.87 7	99.90 20

agg_prior2														97.84 42	99.87 7	99.91 18

agg_prior					99.54 36	92.66 84		97.64 89		97.98 40			99.61 89

test_prior4						92.00 94	99.41 54

test_prior				97.01 62	99.58 29	91.77 95		97.57 107					99.49 104			99.79 34

旧先验2							98.67 135		85.75 225	98.96 12			98.97 142	93.84 115

新几何2							98.26 184

无先验							98.52 153	97.82 55	87.20 202				99.90 40	87.64 185		99.85 29

原ACMM2							98.69 131

testdata2													99.88 44	84.16 222

testdata1							97.89 208		92.43 63

plane_prior7					93.84 244	85.73 241

plane_prior5								96.30 212					97.75 195	93.46 122	86.17 219	92.67 228

plane_prior4											96.52 176

plane_prior3						85.91 236			93.65 41	86.99 198

plane_prior2							99.02 97		93.38 46

plane_prior1					93.90 243

plane_prior						86.07 233	99.14 84		93.81 39						86.26 218

n2								0.00 370

nn								0.00 370

door-mid								84.90 358

test11								97.68 81

door								85.30 357

HQP5-MVS						86.39 221

HQP-NCC					93.95 237		99.16 75		93.92 31	87.57 192

ACMP_Plane					93.95 237		99.16 75		93.92 31	87.57 192

BP-MVS														93.82 117

HQP4-MVS										87.57 192			97.77 190			92.72 226

HQP3-MVS								96.37 208							86.29 216

NP-MVS					93.94 240	86.22 227					96.67 174

ACMMP++_ref															82.64 247

ACMMP++															83.83 235

High-resolution multi-view benchmark